Hidenori Onishi
Papers
1
Total Citations
2
H-Index
1
About
Hidenori Onishi is a researcher in robotics and intelligent control systems, with a focus on neural network-based approaches for real-time robotic applications. His most-cited work, "Tracking control for robot arm using neural network with simultaneous perturbation learning rule" (2003), introduces a novel neuro-controller that leverages the simultaneous perturbation (SP) learning rule. This method requires only two evaluations of an error function per weight update, significantly reducing computational overhead compared to traditional gradient-based techniques. Onishi’s contribution lies in demonstrating that efficient, model-free learning can achieve precise tracking control for robot arms, making it practical for systems with limited computational resources or unknown dynamics. While his citation count is modest, the work is notable for its early application of SP learning in robotics, a technique that has since gained traction in adaptive control and optimization. Onishi’s research bridges theoretical learning algorithms with tangible robotic systems, offering a foundation for future work in lightweight, real-time neuro-controllers. His approach remains relevant for students and engineers exploring efficient learning in autonomous systems.
Research Focus
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Top Papers
- 1